RI: Integrating Illumination, Motion and Shape Models for Video Analysis

RI：集成照明、运动和形状模型以进行视频分析

• 批准号: 0712253
• 负责人: Amit Roy-Chowdhury
• 金额: $ 39.69万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0712253&HistoricalAwards=false
• 关键词: RI; Integrating; Illumination; Motion; Shape

Proposal 0712253 PI: Amit Roy ChowdhuryInstitution: University of California - RiversideCo-PI: Gopi MeenakshisundaramInstitution: University of California - IrvineTitle: Integrating Illumination, Motion and Shape Models for Video AnalysisThis project involves a fundamental research program in video analysis that is built upon a novel framework for integrating different independent modalities of image formation - illumination conditions, object shape, motion and surface reflectance. We focus on the problem of pose and illumination invariant object recognition in video, based on learned models of lighting, motion and shape. We also develop novel scene relighting methods using the illumination models learned from natural videos. The proposed research proceeds by first developing a mathematical framework that relates the appearance of a video sequence with the lighting conditions, motion of the objects being imaged, and their shape and surface properties. Thereafter, it addresses the inverse problem of recognition from video. The overall mathematical approach is to combine precise geometrical models with statistical data analysis tools, thus combining accuracy and robustness.This research will benefit a large number of existing applications and create new ones that rely on efficient object tracking under changing environmental conditions. They include applications in national priority areas like homeland security, monitoring of nuclear installations and border security, in commercial interests like video communications, multimedia databases and entertainment, in social causes like wildlife and environmental monitoring, and in medical and biological applications relying on video analysis. Scene relighting by learning the motion and illumination of objects from natural videos will also have a significant impact in creation of realistic virtual environments.Progress on this project will be regularly updated at http://www.ee.ucr.edu/~amitrc/JMIS.htm

提案0712253 PI：Amit Roy Chowdhury机构：加州大学河滨分校Co-PI：Gopi Meenakshisundaram机构：加州大学欧文分校职务：整合照明，运动和形状模型的视频分析这个项目涉及到一个基础研究计划，在视频分析是建立在一个新的框架，整合不同的独立模式的图像形成-照明条件，物体形状，运动和表面反射率。我们专注于视频中的姿态和光照不变的对象识别的问题，学习模型的基础上，照明，运动和形状。我们还开发了新的场景重新照明方法，使用从自然视频中学习的照明模型。所提出的研究的收益，首先开发一个数学框架，涉及的外观的视频序列与照明条件，运动的对象被成像，以及它们的形状和表面特性。此后，它解决了从视频识别的逆问题。 整体的数学方法是将联合收割机精确的几何模型与统计数据分析工具相结合，从而将精确性和鲁棒性结合起来。这项研究将使大量现有的应用程序受益，并创造出在不断变化的环境条件下依赖于高效目标跟踪的新应用程序。它们包括在国家优先领域的应用，如国土安全、核设施监测和边境安全，在商业利益方面的应用，如视频通信、多媒体数据库和娱乐，在社会事业方面的应用，如野生动物和环境监测，以及在依赖于视频分析的医学和生物学应用。通过从自然视频中学习物体的运动和照明来重新照明场景，也将对创建逼真的虚拟环境产生重大影响。该项目的进展将定期在http://www.ee.ucr.edu/~amitrc/JMIS.htm上更新。